1. Field of the Invention
The present invention relates to the development of underground reservoirs, such as hydrocarbon reservoirs, or acid gas storage sites. In particular, the invention relates to a method for development optimization according to a production scheme for an underground medium containing fluids.
2. Description of the Prior Art
Exploration and development of reservoirs, notably petroleum reservoirs, requires knowledge of the underground geology as precise as possible to efficiently provide reserve evaluation, production modelling or development management. Indeed, determining the location of a production well or of an injection well, the drilling mud composition, the completion characteristics, selecting a hydrocarbon recovery method (such as waterflooding for example) and the parameters required to implement this method (such as injection pressure, production flow rate, etc.) requires good knowledge of the reservoir. Knowledge of the reservoir notably requires knowing the petrophysical properties of the subsoil at any point in space and being able to predict the flows likely to occur therein.
The petroleum industry has therefore combined for a long time field (in-situ) measurements with experimental modelling (performed in the laboratory) and/or numerical modelling (using softwares). Petroleum reservoir modelling thus is a technical stage that is essential for any reservoir exploration or development procedure. The goal of such modelling is to provide a description of the reservoir.
In order to best develop underground media, the petroleum industries define models allowing better control of development oil fields. The precision of the flow simulations is extremely important since the results of the simulations helps make technical and economic decisions relative to the field development. The models used are generally uncertain due to the lack of data which results in many iterative techniques being used to quantify these uncertainties and/or to calibrate the simulation results relative to the well history. The models derived therefrom are then more reliable and they can be used to predict the future production of the field.
The simulation chain conventionally used in a reservoir study involves the elements presented hereafter.
In a first stage, a geological model is constructed. This model is a three-dimensional computer-implemented numerical model representative of the spatial distribution of the petrophysical properties (porosity, permeability, initial oil saturation for example) in the geological formation of interest. Geostatistical techniques are often applied to generate the petrophysical properties in the geological model. In general, this model comprises a number of cells that is much too large to allow direct simulation of the fluid flows. The required computation times are difficult to handle in practice.
The second stage which is upscaling defines a second model, referred to as reservoir model, from the geological model. The reservoir model is constructed on a coarser grid than the geological model and comprises a more limited number of cells. The goal of the upscaling process is to transfer the petrophysical properties of the geological model to the reservoir model. It determines equivalent properties to be assigned to the coarse cells of the reservoir model from knowledge of the petrophysical properties of the fine cells of the geological model.
The third stage solves the flow equations for the reservoir model created with a computer-implemented flow simulator (software). It is thus possible to estimate how the fluids flow according to the petrophysical properties.
Each stage of the reservoir study requires many repetitions of the simulation chain. Indeed, this sequence of simulation modules is repeated several times, either within the context of a sensitivity analysis or of history matching for optimizing the development of a field. In these various cases, the same sequence is repeated with different parameters occurring in the various simulation modules (geostatistics, upscaling, flow simulation). The sequence is repeated entirely from the time when one parameter at least is modified. If a parameter varies at the level of the geological model construction, all the components of the sequence are relaunched.